import torch
import os

output_model_path = "fsnet_save/fsnet.params"

# dataset
batch_size = 32
truncate_num = 1000
offset = 1460
print("batch size:", batch_size)

# training
resume_model = True
learning_rate = 1e-3
dropout_rate = 0.1
alpha = 0.01
num_epoch = 100
print("learning rate:", learning_rate)


# evaluate
only_eval = True
if only_eval:
    resume_model = True

# device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("device:", device)

# model
num_class = 95
feature_num = truncate_num
hidden_dim = 8
vocab_size = 300
GRU_layer_num = 2
GRU_dirction_num = 2
GRU_bidirection = True if GRU_dirction_num == 2 else False

# create dir
if not os.path.isdir(os.path.dirname(output_model_path)):
    os.mkdir(os.path.dirname(output_model_path))
    print("making dir:", os.path.dirname(output_model_path))
